Clustering strategies for cluster timestamps

Visualization tools that illustrate communication in parallel programs use Fidge/Mattern timestamps to efficiently answer precedence queries. These timestamps have poor execution efficiency when the number of processes is large, limiting the scalability of the tool. Self-organizing hierarchical cluster timestamps can scale if the clusters they use capture communication locality. However, no clustering algorithm has been presented that enables these timestamps to work. We evaluate two clustering strategies for such timestamps, one static and one dynamic. The static algorithm was chosen to demonstrate an unproven assumption of cluster timestamps, namely that good clustering will always yield significant space saving, and to demonstrate that it is possible to select a range of cluster sizes that provide such a savings. We then assessed the merge-on-N^th-communication approach. In all but two cases it provides a timestamp size that is with 20% of the best achievable. We present detailed results for the strategies evaluated.